Overview

Dataset statistics

Number of variables11
Number of observations590
Missing cells1184
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.5 KiB
Average record size in memory84.2 B

Variable types

DateTime1
Numeric10

Dataset

DescriptionReports of cleaned oil dataset
URL

Variable descriptions

Leaded Regular Gasoline, U.S. City Average Retail PricePrice of leaded fuel, 0 when discontinued
Unleaded Regular Gasoline, U.S. City Average Retail PricePrice of unleaded fuel, 0 when leaded was used
Unleaded Premium Gasoline, U.S. City Average Retail PricePrice of premium unleaded fuel
All Grades of Gasoline, U.S. City Average Retail PriceCumulative of all grades of gasoline
Regular Motor Gasoline, Conventional Gasoline Areas, Retail PricePrice of fuel, non-blended
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail PricePrice of fuel, blended
Regular Motor Gasoline, All Areas, Retail PriceCumulative mean of all categories
On-Highway Diesel Fuel PricePrice for diesel fuel

Alerts

Year is highly correlated with Leaded Regular Gasoline, U.S. City Average Retail Price and 7 other fieldsHigh correlation
Leaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 3 other fieldsHigh correlation
Unleaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Unleaded Premium Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
All Grades of Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, All Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
On-Highway Diesel Fuel Price is highly correlated with Year and 6 other fieldsHigh correlation
Year is highly correlated with Leaded Regular Gasoline, U.S. City Average Retail Price and 7 other fieldsHigh correlation
Leaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 2 other fieldsHigh correlation
Unleaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Unleaded Premium Gasoline, U.S. City Average Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
All Grades of Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, All Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
On-Highway Diesel Fuel Price is highly correlated with Year and 6 other fieldsHigh correlation
Year is highly correlated with Leaded Regular Gasoline, U.S. City Average Retail Price and 7 other fieldsHigh correlation
Leaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with YearHigh correlation
Unleaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Unleaded Premium Gasoline, U.S. City Average Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
All Grades of Gasoline, U.S. City Average Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, All Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
On-Highway Diesel Fuel Price is highly correlated with Year and 6 other fieldsHigh correlation
Year is highly correlated with Leaded Regular Gasoline, U.S. City Average Retail Price and 7 other fieldsHigh correlation
Leaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 3 other fieldsHigh correlation
Unleaded Regular Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Unleaded Premium Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
All Grades of Gasoline, U.S. City Average Retail Price is highly correlated with Year and 7 other fieldsHigh correlation
Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
Regular Motor Gasoline, All Areas, Retail Price is highly correlated with Year and 6 other fieldsHigh correlation
On-Highway Diesel Fuel Price is highly correlated with Year and 6 other fieldsHigh correlation
Unleaded Regular Gasoline, U.S. City Average Retail Price has 36 (6.1%) missing values Missing
Unleaded Premium Gasoline, U.S. City Average Retail Price has 104 (17.6%) missing values Missing
All Grades of Gasoline, U.S. City Average Retail Price has 60 (10.2%) missing values Missing
Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price has 228 (38.6%) missing values Missing
Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price has 264 (44.7%) missing values Missing
Regular Motor Gasoline, All Areas, Retail Price has 228 (38.6%) missing values Missing
On-Highway Diesel Fuel Price has 264 (44.7%) missing values Missing
Date has unique values Unique
Leaded Regular Gasoline, U.S. City Average Retail Price has 370 (62.7%) zeros Zeros

Reproduction

Analysis started2022-08-09 18:53:47.731019
Analysis finished2022-08-09 18:54:02.665830
Duration14.93 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Minimum1973-01-01 00:00:00
Maximum2022-02-01 00:00:00
2022-08-09T11:54:02.742865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:03.087361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.084746
Minimum1973
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2022-08-09T11:54:03.301360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1975
Q11985
median1997
Q32009
95-th percentile2019
Maximum2022
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.20476992
Coefficient of variation (CV)0.007112752701
Kurtosis-1.200114098
Mean1997.084746
Median Absolute Deviation (MAD)12
Skewness0.0006149448596
Sum1178280
Variance201.7754885
MonotonicityIncreasing
2022-08-09T11:54:03.493259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197312
 
2.0%
201012
 
2.0%
200012
 
2.0%
200112
 
2.0%
200212
 
2.0%
200312
 
2.0%
200412
 
2.0%
200512
 
2.0%
200612
 
2.0%
200712
 
2.0%
Other values (40)470
79.7%
ValueCountFrequency (%)
197312
2.0%
197412
2.0%
197512
2.0%
197612
2.0%
197712
2.0%
197812
2.0%
197912
2.0%
198012
2.0%
198112
2.0%
198212
2.0%
ValueCountFrequency (%)
20222
 
0.3%
202112
2.0%
202012
2.0%
201912
2.0%
201812
2.0%
201712
2.0%
201612
2.0%
201512
2.0%
201412
2.0%
201312
2.0%

Month
Real number (ℝ≥0)

Distinct12
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.483050847
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:03.645267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.461486012
Coefficient of variation (CV)0.5339285614
Kurtosis-1.220681216
Mean6.483050847
Median Absolute Deviation (MAD)3
Skewness0.004162902133
Sum3825
Variance11.98188541
MonotonicityNot monotonic
2022-08-09T11:54:03.765291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
150
8.5%
250
8.5%
349
8.3%
449
8.3%
549
8.3%
649
8.3%
749
8.3%
849
8.3%
949
8.3%
1049
8.3%
Other values (2)98
16.6%
ValueCountFrequency (%)
150
8.5%
250
8.5%
349
8.3%
449
8.3%
549
8.3%
649
8.3%
749
8.3%
849
8.3%
949
8.3%
1049
8.3%
ValueCountFrequency (%)
1249
8.3%
1149
8.3%
1049
8.3%
949
8.3%
849
8.3%
749
8.3%
649
8.3%
549
8.3%
449
8.3%
349
8.3%

Leaded Regular Gasoline, U.S. City Average Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Price of leaded fuel, 0 when discontinued

Distinct182
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3375457627
Minimum0
Maximum1.354
Zeros370
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:03.926289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.66575
95-th percentile1.207
Maximum1.354
Range1.354
Interquartile range (IQR)0.66575

Descriptive statistics

Standard deviation0.4681040839
Coefficient of variation (CV)1.386787025
Kurtosis-0.8931807439
Mean0.3375457627
Median Absolute Deviation (MAD)0
Skewness0.877057631
Sum199.152
Variance0.2191214334
MonotonicityNot monotonic
2022-08-09T11:54:04.127289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0370
62.7%
0.555
 
0.8%
0.5284
 
0.7%
1.1973
 
0.5%
1.2972
 
0.3%
0.62
 
0.3%
0.6072
 
0.3%
0.6262
 
0.3%
0.632
 
0.3%
0.6292
 
0.3%
Other values (172)196
33.2%
ValueCountFrequency (%)
0370
62.7%
0.4021
 
0.2%
0.4181
 
0.2%
0.4371
 
0.2%
0.4652
 
0.3%
0.4912
 
0.3%
0.5284
 
0.7%
0.5321
 
0.2%
0.5331
 
0.2%
0.5342
 
0.3%
ValueCountFrequency (%)
1.3541
0.2%
1.3521
0.2%
1.3511
0.2%
1.3441
0.2%
1.3351
0.2%
1.3331
0.2%
1.3241
0.2%
1.3211
0.2%
1.3151
0.2%
1.311
0.2%

Unleaded Regular Gasoline, U.S. City Average Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Price of unleaded fuel, 0 when leaded was used

Distinct474
Distinct (%)85.6%
Missing36
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1.773546931
Minimum0.592
Maximum4.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:04.304259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.592
5-th percentile0.66665
Q11.11925
median1.327
Q32.48375
95-th percentile3.5907
Maximum4.09
Range3.498
Interquartile range (IQR)1.3645

Descriptive statistics

Standard deviation0.9010155023
Coefficient of variation (CV)0.5080302564
Kurtosis-0.5891162738
Mean1.773546931
Median Absolute Deviation (MAD)0.3955
Skewness0.7944872424
Sum982.545
Variance0.8118289354
MonotonicityNot monotonic
2022-08-09T11:54:04.457453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9554
 
0.7%
1.1314
 
0.7%
1.1273
 
0.5%
1.2073
 
0.5%
1.1363
 
0.5%
1.1773
 
0.5%
1.2553
 
0.5%
1.253
 
0.5%
1.2413
 
0.5%
0.6653
 
0.5%
Other values (464)522
88.5%
(Missing)36
 
6.1%
ValueCountFrequency (%)
0.5921
0.2%
0.5941
0.2%
0.62
0.3%
0.6051
0.2%
0.6161
0.2%
0.6231
0.2%
0.6261
0.2%
0.6271
0.2%
0.6281
0.2%
0.6292
0.3%
ValueCountFrequency (%)
4.091
0.2%
4.0651
0.2%
3.9331
0.2%
3.9271
0.2%
3.8681
0.2%
3.8561
0.2%
3.8161
0.2%
3.7921
0.2%
3.7862
0.3%
3.7641
0.2%

Unleaded Premium Gasoline, U.S. City Average Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Price of premium unleaded fuel

Distinct428
Distinct (%)88.1%
Missing104
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean2.15823251
Minimum0.98
Maximum4.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:04.625418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.115
Q11.33625
median1.6805
Q33.00075
95-th percentile3.9555
Maximum4.35
Range3.37
Interquartile range (IQR)1.6645

Descriptive statistics

Standard deviation0.9755398661
Coefficient of variation (CV)0.4520086976
Kurtosis-1.079366983
Mean2.15823251
Median Absolute Deviation (MAD)0.51
Skewness0.6107838416
Sum1048.901
Variance0.9516780304
MonotonicityNot monotonic
2022-08-09T11:54:04.771456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4213
 
0.5%
1.4113
 
0.5%
1.3673
 
0.5%
1.2683
 
0.5%
1.363
 
0.5%
1.4412
 
0.3%
1.3232
 
0.3%
1.3392
 
0.3%
1.2822
 
0.3%
1.12
 
0.3%
Other values (418)461
78.1%
(Missing)104
 
17.6%
ValueCountFrequency (%)
0.981
0.2%
0.9841
0.2%
0.9871
0.2%
0.9991
0.2%
1.0071
0.2%
1.011
0.2%
1.0451
0.2%
1.0471
0.2%
1.0521
0.2%
1.0611
0.2%
ValueCountFrequency (%)
4.351
0.2%
4.3191
0.2%
4.2441
0.2%
4.1941
0.2%
4.1921
0.2%
4.1481
0.2%
4.141
0.2%
4.1381
0.2%
4.1021
0.2%
4.11
0.2%

All Grades of Gasoline, U.S. City Average Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Cumulative of all grades of gasoline

Distinct441
Distinct (%)83.2%
Missing60
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean1.865362264
Minimum0.629
Maximum4.142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:04.933456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.629
5-th percentile0.91415
Q11.18675
median1.3795
Q32.605
95-th percentile3.6613
Maximum4.142
Range3.513
Interquartile range (IQR)1.41825

Descriptive statistics

Standard deviation0.9008868246
Coefficient of variation (CV)0.4829554248
Kurtosis-0.6786153734
Mean1.865362264
Median Absolute Deviation (MAD)0.4015
Skewness0.7679061702
Sum988.642
Variance0.8115970708
MonotonicityNot monotonic
2022-08-09T11:54:05.095461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2225
 
0.8%
1.1935
 
0.8%
1.3534
 
0.7%
1.3184
 
0.7%
1.1993
 
0.5%
1.2233
 
0.5%
1.2443
 
0.5%
1.1863
 
0.5%
1.2433
 
0.5%
1.2333
 
0.5%
Other values (431)494
83.7%
(Missing)60
 
10.2%
ValueCountFrequency (%)
0.6292
0.3%
0.6312
0.3%
0.6371
0.2%
0.6451
0.2%
0.6551
0.2%
0.6631
0.2%
0.6691
0.2%
0.6711
0.2%
0.6761
0.2%
0.6851
0.2%
ValueCountFrequency (%)
4.1421
0.2%
4.1151
0.2%
3.9821
0.2%
3.9761
0.2%
3.9181
0.2%
3.9081
0.2%
3.8631
0.2%
3.8392
0.3%
3.8381
0.2%
3.8131
0.2%

Regular Motor Gasoline, Conventional Gasoline Areas, Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Price of fuel, non-blended

Distinct331
Distinct (%)91.4%
Missing228
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean2.098372928
Minimum0.9
Maximum4.002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:05.262605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.03915
Q11.22175
median2.105
Q32.76875
95-th percentile3.6048
Maximum4.002
Range3.102
Interquartile range (IQR)1.547

Descriptive statistics

Standard deviation0.8629110184
Coefficient of variation (CV)0.4112286271
Kurtosis-1.112339466
Mean2.098372928
Median Absolute Deviation (MAD)0.757
Skewness0.3317638835
Sum759.611
Variance0.7446154256
MonotonicityNot monotonic
2022-08-09T11:54:05.407641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0784
 
0.7%
1.0663
 
0.5%
2.4883
 
0.5%
2.2113
 
0.5%
1.0582
 
0.3%
2.482
 
0.3%
2.6782
 
0.3%
1.0822
 
0.3%
1.1992
 
0.3%
3.1682
 
0.3%
Other values (321)337
57.1%
(Missing)228
38.6%
ValueCountFrequency (%)
0.91
0.2%
0.9171
0.2%
0.9231
0.2%
0.9611
0.2%
0.9791
0.2%
0.9941
0.2%
0.9981
0.2%
1.0062
0.3%
1.0081
0.2%
1.0091
0.2%
ValueCountFrequency (%)
4.0021
0.2%
3.9891
0.2%
3.8491
0.2%
3.8371
0.2%
3.8011
0.2%
3.7741
0.2%
3.7461
0.2%
3.741
0.2%
3.7351
0.2%
3.7091
0.2%

Regular Motor Gasoline, Reformulated Gasoline Areas, Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Price of fuel, blended

Distinct311
Distinct (%)95.4%
Missing264
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean2.369110429
Minimum0.984
Maximum4.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:05.567610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.984
5-th percentile1.11775
Q11.51825
median2.3835
Q33.0165
95-th percentile3.8025
Maximum4.19
Range3.206
Interquartile range (IQR)1.49825

Descriptive statistics

Standard deviation0.8829988867
Coefficient of variation (CV)0.3727132664
Kurtosis-1.155138768
Mean2.369110429
Median Absolute Deviation (MAD)0.7605
Skewness0.1007680195
Sum772.33
Variance0.7796870339
MonotonicityNot monotonic
2022-08-09T11:54:05.718642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1292
 
0.3%
1.4832
 
0.3%
1.52
 
0.3%
1.5432
 
0.3%
2.6632
 
0.3%
1.1142
 
0.3%
1.3072
 
0.3%
2.3432
 
0.3%
3.2022
 
0.3%
2.8322
 
0.3%
Other values (301)306
51.9%
(Missing)264
44.7%
ValueCountFrequency (%)
0.9841
0.2%
1.0081
0.2%
1.0161
0.2%
1.0431
0.2%
1.0451
0.2%
1.0461
0.2%
1.0541
0.2%
1.0571
0.2%
1.0671
0.2%
1.0711
0.2%
ValueCountFrequency (%)
4.191
0.2%
4.1871
0.2%
4.0321
0.2%
4.0251
0.2%
4.0171
0.2%
3.9491
0.2%
3.9391
0.2%
3.9191
0.2%
3.9141
0.2%
3.861
0.2%

Regular Motor Gasoline, All Areas, Retail Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Cumulative mean of all categories

Distinct322
Distinct (%)89.0%
Missing228
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean2.142513812
Minimum0.921
Maximum4.062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:06.018963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.921
5-th percentile1.0471
Q11.23125
median2.182
Q32.80825
95-th percentile3.66045
Maximum4.062
Range3.141
Interquartile range (IQR)1.577

Descriptive statistics

Standard deviation0.8830022923
Coefficient of variation (CV)0.4121337689
Kurtosis-1.151839223
Mean2.142513812
Median Absolute Deviation (MAD)0.785
Skewness0.2915628142
Sum775.59
Variance0.7796930483
MonotonicityNot monotonic
2022-08-09T11:54:06.160965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1144
 
0.7%
1.0784
 
0.7%
2.8363
 
0.5%
1.1313
 
0.5%
1.1113
 
0.5%
1.0623
 
0.5%
2.5553
 
0.5%
1.9692
 
0.3%
1.3972
 
0.3%
2.8592
 
0.3%
Other values (312)333
56.4%
(Missing)228
38.6%
ValueCountFrequency (%)
0.9211
0.2%
0.9391
0.2%
0.9451
0.2%
0.9821
0.2%
0.9951
0.2%
0.9981
0.2%
1.0061
0.2%
1.0081
0.2%
1.0092
0.3%
1.0131
0.2%
ValueCountFrequency (%)
4.0621
0.2%
4.0541
0.2%
3.9061
0.2%
3.91
0.2%
3.8521
0.2%
3.8491
0.2%
3.81
0.2%
3.7791
0.2%
3.7661
0.2%
3.7461
0.2%

On-Highway Diesel Fuel Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Price for diesel fuel

Distinct301
Distinct (%)92.3%
Missing264
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean2.445389571
Minimum0.959
Maximum4.703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2022-08-09T11:54:06.318928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.959
5-th percentile1.085
Q11.42525
median2.5105
Q33.12825
95-th percentile3.996
Maximum4.703
Range3.744
Interquartile range (IQR)1.703

Descriptive statistics

Standard deviation1.000537845
Coefficient of variation (CV)0.4091527409
Kurtosis-1.187072514
Mean2.445389571
Median Absolute Deviation (MAD)0.937
Skewness0.1223904225
Sum797.197
Variance1.00107598
MonotonicityNot monotonic
2022-08-09T11:54:06.455964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.123
 
0.5%
3.9053
 
0.5%
2.9972
 
0.3%
2.7852
 
0.3%
1.6372
 
0.3%
1.4822
 
0.3%
2.5192
 
0.3%
3.0452
 
0.3%
1.422
 
0.3%
3.0692
 
0.3%
Other values (291)304
51.5%
(Missing)264
44.7%
ValueCountFrequency (%)
0.9591
0.2%
0.9671
0.2%
0.9731
0.2%
0.9971
0.2%
1.0071
0.2%
1.0221
0.2%
1.0241
0.2%
1.0291
0.2%
1.0391
0.2%
1.0411
0.2%
ValueCountFrequency (%)
4.7031
0.2%
4.6771
0.2%
4.4251
0.2%
4.3021
0.2%
4.1271
0.2%
4.121
0.2%
4.1151
0.2%
4.1111
0.2%
4.0941
0.2%
4.0841
0.2%

Interactions

2022-08-09T11:54:00.256867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.104014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.323737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.479787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.065828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.432667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.710647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.190669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.509984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.832269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.376884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.223018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.426708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.596752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.226608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.547650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.830643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.317636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.632985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.111237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.492832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.346037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.537724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.724788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.380608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.667609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.956639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.435985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.756020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.223239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.622831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.476054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.658750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.863302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.522642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.798646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.096665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.562985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.881025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.342234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.755861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.612052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.777746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.002305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.654643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.927642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.402669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.704019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.008986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.470269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.892864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.737053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.900731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.205302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.796642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.059642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.535670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.835021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.151020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.609295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:01.022863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.854086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.019764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.371310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:52.929646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.193643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.674635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.973985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.283985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.733294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:01.162827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:48.972074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.136733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.700861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.055645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.321609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.807688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.113002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.419985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.862297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:01.297832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.088570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.253765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.823862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.184644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.446610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:55.934670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.244988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.563986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:59.992329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:01.423834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:49.207543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:50.363770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:51.950860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:53.307644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:54.569632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:56.062634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:57.375990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:53:58.696238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-09T11:54:00.128326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-09T11:54:06.586962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-09T11:54:06.879936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-09T11:54:07.165015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-09T11:54:07.455053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-09T11:54:01.632829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-09T11:54:01.916829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-09T11:54:02.213829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-09T11:54:02.495831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateYearMonthLeaded Regular Gasoline, U.S. City Average Retail PriceUnleaded Regular Gasoline, U.S. City Average Retail PriceUnleaded Premium Gasoline, U.S. City Average Retail PriceAll Grades of Gasoline, U.S. City Average Retail PriceRegular Motor Gasoline, Conventional Gasoline Areas, Retail PriceRegular Motor Gasoline, Reformulated Gasoline Areas, Retail PriceRegular Motor Gasoline, All Areas, Retail PriceOn-Highway Diesel Fuel Price
01973-01-01197310.465NaNNaNNaNNaNNaNNaNNaN
11973-02-01197320.491NaNNaNNaNNaNNaNNaNNaN
21973-03-01197330.528NaNNaNNaNNaNNaNNaNNaN
31973-04-01197340.537NaNNaNNaNNaNNaNNaNNaN
41973-05-01197350.550NaNNaNNaNNaNNaNNaNNaN
51973-06-01197360.556NaNNaNNaNNaNNaNNaNNaN
61973-07-01197370.558NaNNaNNaNNaNNaNNaNNaN
71973-08-01197380.554NaNNaNNaNNaNNaNNaNNaN
81973-09-01197390.550NaNNaNNaNNaNNaNNaNNaN
91973-10-011973100.402NaNNaNNaNNaNNaNNaNNaN

Last rows

DateYearMonthLeaded Regular Gasoline, U.S. City Average Retail PriceUnleaded Regular Gasoline, U.S. City Average Retail PriceUnleaded Premium Gasoline, U.S. City Average Retail PriceAll Grades of Gasoline, U.S. City Average Retail PriceRegular Motor Gasoline, Conventional Gasoline Areas, Retail PriceRegular Motor Gasoline, Reformulated Gasoline Areas, Retail PriceRegular Motor Gasoline, All Areas, Retail PriceOn-Highway Diesel Fuel Price
5802021-05-01202150.02.9723.5963.0412.8853.2022.9853.217
5812021-06-01202160.03.1543.8023.2452.9643.2813.0643.287
5822021-07-01202170.03.2333.8973.3263.0443.3393.1363.339
5832021-08-01202180.03.2553.9383.3513.0623.3683.1583.350
5842021-09-01202190.03.2653.9453.3613.0813.3823.1753.384
5852021-10-012021100.03.3854.0403.4773.1933.5063.2913.612
5862021-11-012021110.03.4824.1483.5763.2753.6593.3953.727
5872021-12-012021120.03.4084.1003.5053.1683.6083.3073.641
5882022-01-01202210.03.4134.1023.5003.1873.5953.3153.724
5892022-02-01202220.03.5924.2443.6753.4003.7733.5174.032